About ResearchImpact AI
Accelerating scientific progress by making quality research impact scalable and practical
ResearchImpact AI transforms how academic research demonstrates its value to society. Our mission is to bridge the gap between groundbreaking research and real-world recognition by automating the complex process of impact assessment. We empower researchers and institutions to showcase their contributions to knowledge, policy, and societal progress through AI-powered analysis that generates comprehensive, evidence-based impact reports in minutes rather than weeks. By streamlining this critical but time-intensive process, we enable researchers to focus on what they do best — advancing human knowledge — while ensuring their work receives the recognition and support it deserves for creating meaningful change in the world.
Currently open for beta trial
The Four Core Research Impact Questions
Every research impact assessment addresses four fundamental questions that demonstrate how your work creates meaningful change
What is the problem this research seeks to address and why is it significant?
We analyse the challenge your research tackles, including the populations affected, the magnitude of the issue, and why solving this problem matters for society. This includes examining contextual background, economic burden, and strategic significance within your field.
What are the research outputs of this study?
We evaluate the tangible deliverables and achievements of your research: publications, capacity building, decision support tools, system enhancements, and innovations developed. This demonstrates the concrete contributions your research has made to advancing knowledge and practice.
What impacts has this research delivered to date?
We assess the real-world outcomes and changes your work has achieved, from improved health outcomes and policy changes to technological adoption and environmental benefits. This includes domain-specific impacts, economic value creation, and reputational influence, all supported by quantitative evidence.
What impact from this research is expected in the future?
We project potential long-term outcomes with realistic timeframes, evidence-based rationale, and consideration of implementation pathways and potential barriers. This forward-looking analysis connects your demonstrated achievements to future societal benefits.
Built for Researchers, By Researchers
ResearchImpact AI was developed in partnership with researchers and research administrators who understand the real challenges of impact documentation. Our assessment frameworks draw on established methodologies including the NSW Health Research Impact Assessment Framework (RIAF) and have been refined through trials at leading Australian universities, including the University of Sydney, University of Technology Sydney (UTS), and Monash University.
We've built specialised assessment frameworks for 10+ research domains, from healthcare to climate science to social sciences, each designed with field-specific input to capture how different disciplines create impact. The platform continuously evolves based on researcher feedback, ensuring it meets the diverse needs of the global research community.
Peer-reviewed publications
Ward, J. et al. (2023). Development of a novel and more holistic approach for assessing impact in health and medical research: the Research Impact Assessment Framework.
Ward, J. et al. (2025). Pilot testing of the Research Impact Assessment Framework.
What We Do — and Don't Do
Academics are rightly sceptical of AI claims. Here's an honest account of what this tool does and doesn't do.
| What we do | What we don't do |
|---|---|
| Search publicly available sources — publications, policy documents, media, and citation networks | Access paywalled, confidential, or unpublished data |
| Surface documented impacts you may not have been aware of — including downstream policy citations and media coverage | Invent or embellish impacts with no public record |
| Generate evidence-based narrative reports aligned to assessment frameworks | Replace expert human judgement in evaluation |
| Ground every claim in retrieved sources with traceable citations | Generate unreferenced assertions or fabricate evidence |
| Cut report preparation from weeks to hours | Produce output that can be submitted without researcher review |
| Provide structured evidence spreadsheets (Excel) so you can verify every claim | Guarantee complete coverage of every impact |
| Your research data and reports belong entirely to you | Claim ownership or rights over your research content |
| Process all data using enterprise-grade, stateless APIs | Store, share, or train on your research data |
Always verify AI-generated outputs and references before submission.
Who Benefits from ResearchImpact AI
Research impact assessment demonstrates how academic work creates meaningful change in society and drives scientific progress. It's essential for securing funding, institutional recognition, and policy influence. But the process is intensely time-consuming. Researchers and administrators spend weeks manually compiling evidence, analysing outcomes, and writing reports that prove their work matters.
ResearchImpact AI automates this complex process, generating comprehensive, evidence-based impact reports in minutes rather than weeks. It supports everyone involved in the research lifecycle, from individual researchers to librarians, research administrators, and institutional leadership.
Early & Mid-Career Researchers
Save days of work on ARC DECRA, NHMRC fellowships, and promotion dossiers. Discover hidden impacts you didn't know existed. Build your impact profile systematically from day one of your career.
Principal Investigators & Senior Researchers
Generate evidence-based impact narratives for major grants (ARC Discovery/Linkage, NHMRC, ERC, NIH), promotion to professor, and REF submissions. Document decades of influence efficiently for high-stakes applications.
Research Administrators & Librarians
Support researchers day-to-day without expanding staff. Generate consistent, properly cited reports for grant pipelines and promotion rounds. Librarians can use the tool to help researchers surface impact evidence they didn't know existed — policy citations, media coverage, and downstream adoption across disciplines.
Universities & Research Support Teams
Manage institution-wide reporting cycles with consistency and scale. Generate faculty-level impact portfolios for ARC, NHMRC, NIH, REF, ERC, AACSB, and EQUIS submissions. Provide leadership with a clear view of research strengths across departments, and ensure every researcher is ready when assessment deadlines arrive.
How Our Technology Works
ResearchImpact AI employs an AI-powered pipeline combining multiple technologies
Multi-Source Data Retrieval
Automatic search and retrieval from publication databases, peer-reviewed journals, citation networks, researcher profiles, policy documents, news articles, and industry reports — building a comprehensive evidence base for your research.
Context Analysis & Evidence Gathering
AI models analyse the broader context of your research: problem identification, field-specific background, related work and trends, knowledge gaps, and evidence gathering from authoritative sources with proper citation tracking.
Comprehensive Impact Analysis
Rigorous evaluation across multiple impact dimensions using domain-specific outcome measures, economic value assessment, organisational and reputational influence evaluation, and future impact projections with evidence-based rationale.
Report Generation & Structured Outputs
Evidence-based narratives synthesised into submission-ready reports addressing all four core research questions. Delivered as Word documents and Markdown, with an executive summary, detailed section breakdowns, and structured Excel spreadsheets for verification and reuse.
Domain Intelligence Layer
10 field-specific templates — focus areas, indicators, metrics, and research questions
Source Ingestion
Publications, PDFs, and datasets ingested
Evidence Discovery
Agents find missing evidence and fill gaps
Evidence Assembly
Evidence assembled into a unified base
Domain Calibration
Tailored to your field's frameworks and style
Report Synthesis
Content, references, and section-level validation
Intelligence is distributed across every stage — each task matched to the best model, using enterprise-grade LLMs only.
All analysis uses state-of-the-art large language models combined with retrieval-augmented generation (RAG) technology to ensure accuracy, relevance, and proper attribution of sources. The system maintains strict citation standards throughout.
ResearchImpact AI is designed to enhance, not replace, human judgement in research assessment. Our technology automates the time-intensive process of searching across databases, compiling evidence, and synthesising findings. Following the San Francisco Declaration on Research Assessment (DORA) and Leiden Manifesto principles, we believe that "no numbers without stories, and no stories without numbers." The system helps researchers efficiently discover and document their impact whilst maintaining the qualitative expert assessment essential to responsible evaluation.
The system searches publicly available information and may not capture complete research impact, particularly informal influences, undocumented impacts, or confidential applications. Researchers should always verify AI-generated outputs and references before submission, adding their expert interpretation, contextual knowledge, and any additional evidence to create a comprehensive impact narrative.
Architectural Design Philosophy
Six principles governing every design decision in the system
Context First
Better context beats a better model. Output quality is determined by the richness of the context, not the size of the model.
Orchestration, Not End-to-End Generation
A rich, structured context is assembled from independent sources before the synthesis model is called. Intelligence is distributed across the pipeline.
Separation of Concerns and Modularity
Each module has a single responsibility: retrieval retrieves, context generation contextualises, content generation writes. Every module is isolated, independently testable, and replaceable without affecting the rest of the pipeline.
Design for Failure
Every operation has a defined fallback: oversized context triggers chunk-and-combine, a failed PDF falls back to abstract-only, a malformed response triggers partial recovery. The system is built to deliver output, not to fail quietly.
Responsible AI by Design
Enterprise-grade providers only. All processing is stateless: no user data persists beyond the API call. Uploads require explicit consent, making data governance a structural property, not a policy assumption.
Traceability & Consistency
Every result is fully auditable. Any report can be traced back to its original parameters. Every input, source, and parameter is logged, so any result can be interrogated and verified.